Studies and samples

Lecture 5

Aidan Combs

Duke University
SOCIOL 333 - Summer Term 1 2023

2023-05-24

Today

  • Relationships between variables
  • Studies and samples
  • Questions on the project

Relationships between variables: association vs causation

Association

  • Description
  • Is variable 1 related to variable 2?

Association

Causation

  • Explanation
  • Does variable 1 lead to variable 2?

Explanatory and response variables

  • Explanatory variable -> might affect -> response variable

Examples

What are the explanatory and response variables?

  • How are college students’ opinions on binge drinking affected by the binge drinking behaviors of their friends?
  • How does the likelihood of stent placement vary by gender identity?

Explanatory does not equal explanation

  • We often use social categories like race, gender, ethnicity, sexuality, etc as explanatory variables
  • But the categories don’t cause differences; stigma and discrimination and unequal access to power and resources do
  • Ex: How do rates of high blood pressure vary by race?
  • Differences are caused by racism (structural and interpersonal), not race

Types of studies

Observational studies

  • Researcher doesn’t make any changes; just collects data

  • Examples

    • A survey of peoples’ opinions on police reform
    • Data on the race, age, and gender of people who were hired by a firm

Experiments

  • Researcher assigns participants into different conditions/treatments
  • Control: Everything except one variable is the same between treatments
  • Randomization: If people are randomly assigned to conditions, then groups in different conditions are similar
  • Differences have to be because of the treatment!

Experiments

  • Recording how much money people leave in a tip jar depending on whether there’s a poster with eyes on it on the wall
  • Measuring opinions about the other political party after reading either a news article a participant agrees with or doesn’t agree with

Why does the distinction matter? Back to association vs causation.

  • Observational studies: associations are easy; causation is hard

    • Is smoking causing cancer, or do people already prone to cancer just happen to smoke more?
    • Does social media cause anxiety, or do people with anxiety use social media more?

  • Experiments: If everything is the same except for the treatment, then differences have to be because of the treatment

    • But is that effect important in real life, where things aren’t held to be the same?
    • Experiments are hard and not always ethical!

Exercise 1

Populations and Samples

Sampling

  • You don’t need to eat all the soup to check the seasoning

  • But you do need to make sure it’s stirred up

Target population

  • The group you’re interested in
  • Determined by your research question
  • How is time spent on social media related to likelihood of being diagnosed with anxiety among American teenagers?
  • How is a country’s spending on healthcare related to its average life expectancy?

Sample

  • Who your data points are actually from
  • Determined by your data set
  • A survey of 2500 people recruited on the subreddit r/financialindependence
  • Salary and demographic data for all employees at three engineering firms in the Midwest

Sampling frame

Sampling frame

  • Who could have been included in your sample?
  • Determined by the sampling process
  • A survey emailed to all sophomores at Duke
  • A survey posted on r/financialindependence
  • Often this is not clearly stated in publicly available data sets :(

Random and nonrandom samples

  • Random avoids bias, but it’s not always possible
  • Nonrandom “convenience samples” are common
  • What is the sampling frame?

Representativeness and generalizability

  • Representativeness: How much your sample is like the target population

  • Generalizability (the goal!): The idea that your sample will tell us something about a larger group than just the sample

  • It’s easier to argue that your results are generalizable if your data are representative

  • But non-representative samples can give generalizable results under the right conditions

    • Does the relationship you’re interested in work differently in your sample than in the population?

This is a problem if you want to estimate mental health in the population

But not if you’re interested in the social media/mental health relationship

Interactions are a problem for studying relationships

Project proposal

  • Example is posted
  • Instructions are shortened; bad data sets removed
  • By class tomorrow: a first draft (for feedback; graded for completion only)
  • Questions?

🔗 https://soc333-sum23.github.io/